Moment Condition Models in Empirical Economics

Abstract

In the first chapter of this dissertation, we approach the estimation of dynamic stochastic general equilibrium models through a moments-based estimator, the empirical likelihood. We try to show that this inference process can be a valid alternative to maximum likelihood. The empirical likelihood estimator only requires knowledge about the moments of the data generating process of the model. In this context, we exploit the fact that these economies can be formulated as a set of moment conditions to infer on their parameters through this technique. For illustrational purposes, we consider the standard real business cycle model with a constant relative risk adverse utility function and indivisible labour, driven by a normal technology shock. In the second chapter, we explore further aspects of the estimation of dynamic stochastic general equilibrium models using the empirical likelihood family of estimators. In particular, we propose possible ways of tackling the main problems identified in the first chapter. These problems resume to: (i) the possible existence of dependence between the random variables; (ii) the definition of moment conditions in the dynamic stochastic general equilibrium models setup; (iii) the alternatives to the data generation process used in the first chapter. In the third chapter, we investigate the short run effects of macroeconomic and fiscal volatility on the decision of the policy maker on how much to consume and how much to invest. To that end, we analyse a panel of 10 EU countries during 1991-2007. Our results suggest that increases in the volatility of regularly collected and cyclical revenues such as the VAT and income taxes tend to tilt the expenditure composition in favour of public investment. In contrast, increases in the volatility of ad hoc -type of taxes such as capital taxes tend to favour public consumption spending, albeit only a little.